Plate Pal

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint/idempotentHint/destructiveHint=false, so the safety profile is covered. The description adds real value beyond that by disclosing the return payload: per 100 g/ml and per serving values, traffic lights for fat/saturates/sugars/salt, Nutri-Score, allergens and a Halal link. It does not say what happens with an unknown barcode, but for a read-only lookup that is a minor gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose, then usage examples, then return contents in three tight sentences with no filler. The opening sentence duplicates the title verbatim, a minor redundancy, but everything else earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the burden of explaining return values and does so thoroughly (units, traffic lights, Nutri-Score, allergens, Halal link). The parameter surface is small and fully covered by the schema, so the definition is nearly complete; only failure/edge-case behaviour is unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both the barcode format (8-14 digits) and the servings range already documented in the schema, so the baseline is 3. The description's mention of 'per serving values' loosely ties to the servings parameter but repeats what the schema description ('number of servings to total up') already conveys, adding little new meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource+scope: nutrition for a packaged product retrieved by barcode, with UK traffic lights. This scope implicitly separates it from the broader get_food_nutrition and get_meal_ideas siblings, but the description never names them, so the differentiation is inferential rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Three concrete example requests ('is this barcode high in sugar?', 'how many calories in this?' after a scan, 'what's the salt in 5000157024886?') give clear situational context for when to reach for this tool. There are no stated exclusions or explicit pointers to the sibling tools, so it stops short of a full routing rule.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare the read-only/idempotent/open-world profile, and the description adds substantial behavior beyond that: data provenance (USDA, Open Food Facts, chain-published figures), region selection (UK vs US by country), explicit fallback to a typical USDA version for unknown chain items, and the null-return contract when a chain publishes no weight. This is exactly the kind of context structured fields cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose is front-loaded in the first sentence, which is good, but the remainder is a single dense block that enumerates many chain examples and edge cases at length. Much of the enumeration repeats the same pattern and could be trimmed without losing information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden itself and does so: per-amount nutrition, per_100 values, portions, UK traffic lights, caffeine_mg for drinks, a chain object with named fields, and items+total for meals. An agent has everything needed to call and interpret the result.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: q may hold a whole meal with quantities, grams/servings may be supplied separately, and country drives which chain figures (GB/IE -> UK) are used. It goes modestly beyond the schema text without fully restating it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource (calories/macros for a named food, dish or product) and scopes it to the amount the user gives, with concrete example inputs. It clearly implies a name-based lookup distinct from a barcode lookup, but it never names the siblings get_barcode_nutrition or get_meal_ideas to draw the boundary explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Rich worked examples establish when the tool applies (single foods, sized portions, chain items, whole meals) and it describes how q vs grams vs servings are combined and overridden. It lacks explicit when-not guidance and any routing to the two sibling tools, so the alternative-selection condition is left implicit.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover the safety profile (readOnly, non-destructive, openWorld, idempotent), but the description adds real behavioral context: ideas rotate daily, weight-loss requests yield lighter filling ideas at 450 calories or less and omit shop links. It's strong disclosure, though it never describes the shape of the response beyond 'rough macros' and shop links.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with purpose, then examples, then boundaries, all in a compact block. It loses a little economy by re-listing the goal/diet/meal values that the schema already enumerates, but every sentence otherwise carries usable information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description compensates by explaining what comes back (rough macros, shop links) and how special cases behave (rotation, weight-loss mode, no shop links). Combined with full schema coverage and safety annotations, an agent has everything needed to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all six parameters are already documented with their defaults and allowed values. The description mostly restates the goal/diet/meal enumerations the schema already contains and only lightly clarifies intended use of q and country. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (get) and resource (simple meal ideas) plus the axes that shape the result (goal, diet, meal, rough macros). This is clearly distinct from the sibling nutrition-lookup tools (get_food_nutrition, get_barcode_nutrition), which fetch data rather than generate suggestions. An agent can route to it without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete triggering phrasings ('high protein halal dinner idea', 'a quick vegan lunch'), enumerates the valid goal/diet/meal values, and explains the q escape hatch for free-form input. It also states the exclusion: no calorie/weight targets, plus the specific weight-loss fallback behavior and a doctor/dietitian pointer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.